Workshop on Modelling Population 24/7

Transcription

1 Workshop on Modelling Population 24/7 26 January 2010

2 Welcome, introductions and context

3 Programme for day Welcome, introductions and context Guest presentations Pop24/7 project overview Lunch Algorithms, data and demonstration Applications, challenges and future work Close 3

4 About ourselves David Martin Samantha Cockings Samuel Leung Population surface models Geo-refer: geographical referencing for social scientists Census output areas and workplace zones 4

5 Housekeeping Refreshments Toilets Emergencies Presentations and questions 5

6 Population24/7 - context ESRC-funded project 1 March Feb 2011 Space-time population modelling 24/7/52 Drawing on census, mid-year population estimates Taking advantage of new data sources Based on existing population surface modelling tools Aiming to develop a generalizable methodology 6

7 Introduction Importance of small area population mapping Deficiencies of current night-time approaches Advantages of gridded population models: esp. stability over time and reconstruction of settlement geography

8 Importance of small area population mapping Resource allocation large areas > small areas Targeting services/marketing Site location decisions/transportation demand Emergency planning Appropriate denominator population counts 8

9 Deficiencies of current night-time approaches Limited data on workplaces, travel to work, student residences No data on other activity patterns Frozen in time - census year/reference date Frozen in space residential base 9

10 Photos: David Martin, Sam Cockings 10

11 Photos: David Martin 11

12 Photos: David Martin 12

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15 Photo: David Martin

16 Advantages of gridded population models Stability over time So very important if many different times represented Reconstruction of underlying settlement and neighbourhood geography/model is not space-filling e.g. Distinction between commercial and residential neighbourhoods Integration with other modelled sources esp. environmental Computational efficiency 16

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19 (Schmitt, 1956, p. 83). One of the most important and difficult problems now facing city planners is the development of accurate, usable techniques for estimating the current daytime population of census tracts in urban areas Why? Reasons included modelling the location and size of bomb shelters and the potential casualties resulting from a nuclear attack Schmitt, R. C. (1956) Estimating Daytime Populations. Journal of the American Planning Association 22 (2), 83-85

20 Challenges to be addressed Night-time grid on its own is not the solution, but can grid be harnessed to this problem? Variety of new data sources becoming available but no integrating modelling framework data review and integration Review contemporary studies of the issues Develop extensible methodology for constructing 24/7 population models 20

21 Aims of today s workshop Share common interests in this problem space Find out more about what others have been doing Demonstrate project work in progress Seek feedback/suggestions/extensions/uses What can we most usefully do from here/are there opportunities for working together? Networking opportunity for diverse communities with acommon interest 21

22 Guest presentations

23 Guest presentations 24-hour Edgware Road National Population Database Staffordshire/HSE

24 Population 24/7 project overview

25 Observations This entire sub-branch of geographical and cartographic enquiry is essentially concerned with mapping the population in bed We have seen enormous advances in geovisualization techniques, computing power and dynamic modelling sophistication We have not adequately tackled the entire area of timespecific population modelling

26 Space-time population modelling ~99% of work based on night-time; ~0.5% daytime? Numerous motivations for time-specific models: emergency planning, transportation, business location, etc. General approach is to start with night-time population map and transfer population subgroups to specific daytime locations, e.g. schools, workplaces Longstanding difficulty of obtaining data with sufficient space/time resolution In reality, many different timescales to be modelled

27 Examples Emergency response in US cities Sleeter and Wood (2006) US hazard exposure and response McPherson et al. (2006) Landscan USA - Bhadhuri et al. (2007) Helsinki - Ahola et al. (2007) UK Health and Safety Executive Smith and Fairburn (2008)

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29 Modelling framework 29

30 Data sources - residential Census- or register-based, using usual place of residence Residence definition equivalent to night-time population locations, students counted at term-time residence Decennial update interval long-term population change UK census at output area level (OA, pop ~300) and official mid-year estimates (MYEs) at Lower Super OA level (LSOA, pop ~1500) Some workplace data from census, but not all people have workplaces and not all workplaces are daytime locations 30

31 Data sources non-residential New administrative sources esp. from government, Neighbourhood Statistics Service (NeSS) Huge growth in availability and frequency since 2001 census Annual Business Inquiry dataset (employers, employees) Schools, hospitals, visitor attractions, long-distance visitor numbers, transportation flows Indirect measures of retail, leisure activity, points of interest Can all be related to NeSS geography hierarchy, NSPD 31

32 Mapping population to activities/places Residential Private dwellings Communal ests. Total population +/- external visitors Nonresidential Education Employment Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalized local...and further subdivisions Transport Road Rail Metro/subway Air Water

33 What do current maps cover? Residential Private dwellings Communal ests. Census, MYE Census, MYE Total population +/- external visitors Nonresidential Education Employment Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalized local Transport Road Rail Metro/subway Air Water

34 Data sources for all non-residential Residential Private dwellings Communal ests. Census, MYE Census, MYE Total population +/- external visitors Nonresidential 24% Education Employment 49% Temp accomm. Healthcare Family/social Retail Leisure Tourism Generalized local NeSS, EduBase Census, ABI, QLFS VisitBritain, ABI HES VisitBritain ABI, commercial DCMS, ALVA, etc. DCMS, ALVA, etc. ID2007, NeSS Transport Road Rail Metro/subway Air Water DfT, AADF National Rail TfL, etc CAA TfL

35 Modelling framework Builds on Martin (1989, 2006), Martin et al. (2000) currently implemented in SurfaceBuilder program One of a variety of methods for reallocation of population counts onto a series of geographical features Akin to dasymetric models where known population counts are allocated to most likely set of spatial locations Adaptive kernel estimation, treating each centroid as a high information point. Weight each cell to receive population reallocated from local centroids, hence volume preserving 35

36 Extension to the spatio-temporal problem Spatial centroid: Population count Spatial extent Time-space centroid: Population capacity Spatial extent Time profile Area of influence e.g. census output area Census population Modelled e.g. primary school Pupil numbers Small (one cell) Term dates, school day Catchment area (modelled time/space) 36

37 Time profile example school Population In transit Present Time of day 37

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39 Treatment of centroid i at time t centroid i background layer b local extent d area of influence j study area a

40 Discussion

41 Implementation, data and demonstration

42 Centroids, boundaries and grids Left: centroid locations and boundaries; Right: centroid populations redistributed onto grid 42

43 Distance decay function Always try to provide a caption next to your picture in this style 43

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45 Overview of Southampton study area 45

46 Centroid set 1696 census OAs 3329 workplaces 211 schools and colleges 2 universities

47 Background layer: transportation network 47

48 Background layer Population capacity Land use Land cover Pop density Transport

49 Basic time-space interpolation algorithm Specify study area a and time t Identify background layer b (cells that can contain population) for time t Adjust for external visitors in/out of a at time t Sum all residential centroids to obtain population P Examine each centroid i to obtain populations p in local extent d and area of influence j at time t Redistribute P across d and j, constrained by b 49

50 Early results Southampton, UK as test area Using existing model with pre-prepared data extracts for specific time slices (SurfaceBuilder program) Does not require full time profiles for each centroid Does not take background layer into account, hence no population is allocated into transport layer Time-space interpolation program currently being written in.net using existing and new code components Preliminary validation against known patterns 50

51 Southampton, 200m cells 02:00 Residential night-time model 51

52 Southampton, 200m cells 08:00 Early workplaces, docks, industrial estates; rest as residential 52

53 Southampton, 200m cells 09:00 Workplaces, educational institutions, daytime model; low residential; very high central densities 53

54 Southampton, 200m cells 18:00 Late workplaces remain, education closed; return to residential; high central densities 54

55 Initial visualization of output Output to kml Multiple layers overlaid in Google Earth 3D navigation and exploration Time slider allows time sequence to be played 55

56 (Schmitt, 1956, p. 83). One of the most important and difficult problems now facing city planners is the development of accurate, usable techniques for estimating the current daytime population of census tracts in urban areas Why? Reasons included modelling the location and size of bomb shelters and the potential casualties resulting from a nuclear attack Schmitt, R. C. (1956) Estimating Daytime Populations. Journal of the American Planning Association 22 (2), 83-85

57 Conventional population data sources Census- or address register-based Residence definition equivalent to nighttime population locations Long update intervals 57

58 Software tool SurfaceBuilder24/7 58

59 Discussion

60 Applications, challenges and future work

61 Questions, discussion.